Store intelligence - in-store analytics

ABSTRACT

Store intelligence—in-store analytics (“in-store analytics”) techniques are provided that, by combining analytics with experience, improve the shopping, managing, monitoring, etc., experience of an end user. In-store analytics can be integrated with workflow for optimizing and assisting prioritizing operations.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application claims priority from U.S. provisional patentapplication Ser. No. 61/768,331, STORE INTELLIGENCE—IN-STORE ANALYTICS,filed Feb. 22, 2013, the entirety of which is incorporated herein bythis reference thereto.

BACKGROUND OF THE INVENTION

1. Technical Field

This invention relates generally to the field of computation of anddisplay of digital analytics regarding tracked and stored digital data.More specifically, this invention relates to providing computation ofand display of digital analytics regarding tracked and stored digitaldata, such as for example store data.

2. Description of the Related Art

Online stores have become ubiquitous in today's economy and culture.Similarly, organizations have automated certain processes regardingcertain assets. For example, an information technology (IT) specialistin an organization can deploy a software application remotely usinginternal servers. A librarian may use an online tool to manage theinventory of books at a given library. For these and other organizationsthat collect, store and provide assets, useful analytics and interactivecapabilities that help to improve vastly an end user's overallexperience in acquiring, monitoring, or managing the assets are sorelylacking or are very limited.

SUMMARY OF THE INVENTION

Store intelligence—in-store analytics (“in-store analytics”) techniquesare provided that, by combining analytics with experience, improve theshopping, managing, monitoring, etc., experience of an end user.In-store analytics can be integrated with workflow for optimizing andassisting prioritizing operations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a screen shot showing a collection of a variety of mobiledevices that are offered at a particular store, according to anembodiment of the invention;

FIG. 2 is a screen shot showing how old the devices of FIG. 1 are,according to an embodiment of the invention;

FIG. 3 is a screen shot showing the number of days since each particularitem was last requested, according to an embodiment of the invention;

FIG. 4A-4B are screen shots showing how many particular items of thestore are in use, according to an embodiment of the invention;

FIG. 5 is a screen shot showing how many times each item has beenrequested, according to an embodiment of the invention;

FIG. 6 is a screen shot showing how many times each item was denied,according to an embodiment of the invention;

FIG. 7 is a screen shot showing the average number of days to complete aprocess regarding each item, according to an embodiment of theinvention;

FIG. 8 is a screen shot showing the risk scores of the items, accordingto an embodiment of the invention; and

FIG. 9 is a block schematic diagram of a system in the exemplary form ofa computer system according to an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Store intelligence—in-store analytics (“in-store analytics”) techniquesare provided that, by combining analytics with experience, improve theshopping, managing, monitoring, etc., experience of an end user.In-store analytics can be integrated with workflow for optimizing andassisting prioritizing operations.

In an embodiment, store intelligence—in-store analytics provides a wayto change a given store on an automatic and/or interactive basis. Forexample, store intelligence allows a manager of a store to determineimmediately in real-time that, based on the analytics, the store needsto change its inventory. In accordance with an embodiment, storeintelligence—in-store analytics may be configured to have the storeautomatically perform a business process such as change itself based onthe analytics, e.g. by automatically removing an item from ato-be-purchased list based on the analytics which indicate a poorperformance in sales of the item.

It should be appreciated that for the purposes of understandingparticular use-cases as depicted in the figures to follow are used asexamples only and are not meant to be limiting. For example, while thediscussion herein may refer to end user as a customer or even as amanager, it should be appreciated that such usages is not meant to belimiting. A store may be an end user.

An end user is meant to mean any entity, e.g. human or machine, thatacts on the target item data and can interact with the target item data.

In an embodiment, in-store analytics may be used with or for but is notlimited to self-service or self-service provisioning and management. Forexample, an embodiment may be applied to online mechanisms or enterpriseplatforms that may already exist, such as for example, IdentityEnforcer, by Avatier in San Ramon, Calif. That is, in this example,in-store analytics can act on, interact with, or modify particular datafrom Identity Enforcer.

An example embodiment may be understood with reference to FIG. 1, astore having mobile devices. It should be appreciated that such store isby way of example only and is not meant to be limiting.

FIG. 1 is an exemplary screenshot showing items of interest, such as forexample, items available or of interest to a store, which may be anonline store. FIG. 1 shows a collection of a variety of mobile devicesthat are offered at a particular store.

It should be appreciated that any collection of items of an interest toan end user may be contemplated in this discussion as well. For example,instead of store, an embodiment may include an entity of interest, suchas but not limited to a secure site or application and instead of storedassets, e.g. items available at the store, an embodiment may includeitems such as representations of individual SAP accesses, Oracleaccesses, active directory accesses, and the like. Other examples of anentity of interest include but are not limited to any item that can berequested or assigned to a target user and the item can be any virtualor physical item that can be assigned to one or more users.

As another example, in-store analytics may perform analytics onparticular data and present results thereof in the context of auditingor compliance. For example, an auditor may be able to quickly determinewhether particular items are available for purchase, have beenpurchased, are no longer being used, how many users per item, and so on.

In an embodiment, in-store analytics can perform the following but isnot limited to these processes:

-   -   counts the number of times the item has been issued;    -   counts the number of times the item has been requested;    -   counts the number of times a request for the item has been        denied;    -   calculates the item's age, e.g. how long the item has been in        the store;    -   calculates the average time to complete a request for said item;    -   calculates the days since last requested;    -   determines who was the last requester; and    -   calculates a risk score for said item.

In an embodiment, the risk for an item is set using a weighted scalethat allows a user to set the range at the container level or at theindividual item level. The system takes into consideration if no scoreis defined at the item. It rolls up to the container level with anactual risk score associated. The risk score is used to determine if anitem is riskier than and the other items. The items can grant levels ofaccess to systems. An example is access to a financial system with theability to write checks would be riskier than an item that allows a userto view the check. The associated risk score of the item is part of theoverall users risk score. If the user has a number of items or more thanusual this will increase the users overall risk in the environment.

In an embodiment, in-store analytics may be applied to third partystores, such as but not limited to Amazon or Sears or Target or anystore, online or otherwise. To implement incorporating in-storeanalytics into a third party application, an embodiment adds GUIelements to the user interface of the third party application via HTML,CSS, and JavaScript changes. A back end web service is implemented tosupply the metrics to in-store analytics engines and storage, andmetrics can be computed therefrom, e.g. SQL can be used to calculatethese metrics. An alternate implementation provides a particularlydesigned browser extension that augments the online store of the targetsite to collect, store, and include this information.

An embodiment provides a different way of displaying information,different from what already exists. What is displayed is not only theresults of analytics, but the ability to drill-down to obtain any levelof analytics desired. For example, each icon in FIG. 1 may be manuallyselected for drilling down to more detailed information about the item.For instance, a user, which may be an individual or a programmedprocessor, may select the iPhone 4 icon, obtain analytics or informationabout the iPhone 4 by drilling-down to another level of detail about theiPhone 4, and based on the analytics or information obtained, perform aspecific task or operation. For example, a store may itself determinethat it needs to order more iPhone 4s or a user may decide to decreasethe order.

In an embodiment, the user finds the target item (iPhone 4 in thisexample) using an existing navigation system. Once the target item is onscreen, the user selects the desired type of analytics metric, e.g.clicks on one of a plurality of store analytic buttons, e.g. how manydays old. The requested data is displayed and the user is free to makedecisions based on this data.

An embodiment provides end users, e.g. a customer, manager, or the storeitself, a new way to look at items such as for example the way items arepurchased, requested, how long they've been in the store, and popularityof an item. An embodiment provides all around analytics and interactivecapabilities.

It should be appreciated that while the discussion herein is aboutonline stores and related actions on items in stores, such as but notlimited to purchasing, requesting, monitoring inventory, etc., suchcontext is by way of example only.

Other applications are included in the scope of the subject matter, suchas but not limited to managing books available in a library, forinstance.

In an embodiment, relational databases are used to store the relevantdata including but not limited to configuration data, customer data, enduser data, data regarding the targeted items of the customers or endusers, and items that are provided by the organization. The relationaldatabases are in communication with analytic servers or engines and withuser interface browsers and applications to achieve presenting resultsand views of the computed analytic data to the end user.

In an embodiment, the configuration data comprise the meta dataconfigured for the different items or objects in the store. This isimportant information/data related to the access that they system isable to track and associate to a user and provide the analytics aboutwhat a user has and what their risk is within the environment. Anembodiment provide granular data that is collected daily and processedto alert the system of what the user has assigned in the connectedsystem versus what the system expects the user to have.

An embodiment can be understood with reference to FIG. 2, a screenshotof the result of an end user viewing another drilling-down level, suchas by double-clicking a particular item of FIG. 1. Thus, for example,FIG. 2 shows another level of detail of the items in a collection. As anexample, FIG. 2 shows the mobile devices that are available in the storeof FIG. 1. In particular, FIG. 2 shows how old the devices are, meaninghow long ago such devices were put into the store. It should beappreciated that embodiments are not limited by the specific example butcan determine and show other information about the items and based onsuch information are configured to allow a suitable action to beperformed. For example, the system may determine that when an item isolder than a number of days, it is removed from the store. As well, anapplication representing the store itself may be configured to removesuch item from the store by removing the item from a particular list, orperform any other suitable operation, when the number of days is tooold.

In an embodiment, a framework is provided that contains built-in toolsthat collect the data from connected systems via integration. Forexample, data and metadata about the target items can be stored andaccessed; data and metadata about customers, e.g. customer profile data,can be stored and accessed; data and metadata about the organization canbe stored and accessed, e.g. rules and constraints regarding processesthe data, e.g. if an item is older than 25 days, remove the item, and soforth.

An embodiment performs any type of analytics of interest on data at thestore, where such data may be stored or collected in real-time or both,and provides the results of such analytics in a variety of manners ofinterest to an end user, such as but not limited to displaying resultsin a display or storing such results in a storage. As well, part of thisprocess is user configurable. That is customers are able to customizeand configure part of this application to suit their business needs. Forexample, a customer can configure the application to change the targetitem's icon color to red when the target item is 25 days old. In anembodiment, a configurable interface and application is provided thatallows a customer to configure the data, analytics, and display by usinga superior point click GUI web based configuration application.

As another example, FIG. 3 shows the results of an embodiment performinganalytics on the store data which are days since each particular itemwas last requested. It should be appreciated that an embodiment, forexample any of the above-mentioned embodiments, provides an end user,such as a shopper, store manager, the store itself, etc., informationaldata reflecting that it has been awhile since anyone bought a particularitem, such as for example, the iPhone 3. In this example, the end usercan determine that the iPhone 4 is the most popular or relevant becauseit has the least number of days since requested. Then, the end user canadd this item to the cart. As another example, a store manager candetermine that it has been over a year, indeed, 459 days since the PalmTreo 680 was requested. Thus, the store manager may decide not to orderany more Palm Treo 680s, for example. Thus, embodiments herein providenew and unexpected results including changing the whole shopping,managing, monitoring, etc., experience for a user such as a customer ormanager that is not currently present in the market or otherwise,because the user is provided with much more meaningful data with whichto make better decisions. The user can glean in an instant, with theassistance and enhancements of visual indicators, which target items aremost popular, least popular, are the oldest, etc.

An embodiment applies analytics to store data and provides for thenumber of usages for each item, i.e. how many particular items are inuse, for example by employees of an organization. An exemplaryscreenshot is shown in FIG. 4A. For example, an office manager orinformation technology (IT) employee can determine that 180 Apple iPhone4s's are in use and that there are only three uses of HTC HD7 Windows.Thus, the IT employee may report back to management that employees usemany more Apple iPhone 4s's than HTC HD7 Windows for example. Fromthere, management can determine whether to continue to use their fundsfor both of these products or to get rid of HTC HD7

Windows and keep Apple iPhone 4s's. Or the data can be used incompliance and auditing processes as another example. FIG. 4B shows theusers that have the assigned item. It should be appreciated that therisk levels of the users are also shown.

An embodiment applies analytics to store data and provides how manytimes a particular item has been requested, for example by customers ofa store or by employees in an organization. An exemplary screenshot isshown in FIG. 5. For example, an end user viewing the sample screen shotas shown in FIG. 5 can determine that the Apple iPhone 4s was requested39 times and the BlackBerry Pearl was requested 12 times. Then, the enduser can make a decision based on that information accordingly. Forexample, perhaps the end user wants more of the BlackBerry Pearls to bein use. Then, based on the analytical information displayed in thescreen shot, the end user can decide to take action to better promotethe BlackBerry Pearl.

It should be appreciated that while analytics, which generate data aboutother individuals that have purchased a particular item, such as forexample by Amazon.com, already exist in the market, determining andproviding the more complete analytics herein including but not limitedto how many times a particular item was purchased, as provided inembodiments herein, is not presently available in the market or in theprior art.

To achieve the results provided by the invention herein and as shown inthe sample figures, an embodiment processes the input data, e.g.regarding age, times requested, times used, etc., and then based on thedata and a particular configuration to give weighting and ranges to thedata, the embodiment displays the data consistently and easily to theend user. An example of this is similar to the risk example above. Acertain object has a perceived risk based on the score assigned to theitem/container. The user making the decision to request the item needsto understand the risk associated to the item and how it may beperceived for their access. Another example is a user who wants to seethe last time an object was requested. This would be useful to determineif the access to the object is really needed or wanted. Another exampleis how long the item typically takes for assignment This informationprovides the user a level expectation (SLA) of when they will beassigned the item and have the access required.

In an embodiment, the results of the analytics may be used eitherpublic-facing or for private purposes. For example, such data may beprovided for store managers, even at a site such as Amazon.com, toobtain more detailed, accurate, timely, and more thorough statistics.

In an embodiment, one or more sorting algorithms are applied, eitherautomatically or can be manually applied. For example, when sorting isautomatic, an embodiment may put the number of items in order by timesrequested. The sort may be from least number of times to greatest numberof times or vice-versa. For example, the end user may want to view theitems by least number of times requested at the top of the list, etc. Itshould be appreciated that for purposes of understanding herein, thenumber of times requested may not mean the number of times purchased.For example, the number of times requested may be greater than thenumber of times actually purchased. For example, a customer may requestan item but may have been denied the item.

In an embodiment, the configuration offers modes for sorting and thenfrom an end user GUI capability the end users are able to sort the dataanalytics to provide the correct and desired sorting. The modes mayinclude but are not limited to the bulleted list of counts andcalculations herein above.

In an embodiment, visual indicators may be used to show an order orparticular importance to an item or one or more collections of items.For example, in the context of mobile devices, when the number ofpurchases of mobile devices from a particular vendor passes a particularthreshold, the color of the icon representing the mobile devices maychange. For example, when the number of purchases of iPhone 4s passesbeyond 50, then the icon representing iPhone 4 may change from grey togreen. As another example, when a particular request to purchase amobile device is denied, the color of the icon representing such mobiledevice may change from blue to red. As another example, in the contextof security and access, when an particular item is not used or accessed,it may have an orange color, for example. Or, as another example, byobserving the color, an end user, such as a manager, may determine thathis collections of iPhone 2s are not being used. It should beappreciated that the use of the color indicator is by way of exampleonly and is not mean to be limiting. Other indicators, such as aspecially marked number of pixels, such as a visual tag, at a top cornerof an icon may be used as an particular indicator.

In an embodiment, the items available for display to the end user andthe configuration settings include but are not limited to:

-   -   Item Age;    -   Last Request;    -   Current Assignment Count;    -   Request Count;    -   Denial Count;    -   Average Request Days; and    -   Risk Score.

In an embodiment, the indicators available for display to the end userand the configuration settings include but are not limited to:

-   -   Metric;    -   Type (Static, Percent);    -   Color Bands (Red, Yellow, Green);    -   Range End;    -   Default Sort (Descending, Ascending); and    -   Reverse.

In an embodiment, the configuration allows the administrator to setupthe different scales and what indicators are available. The end user canselect the view or metric they want to see and select the sorting order.

As another example of a visual indicator, using or overlaying a specialicon, such as a well-known stop sign, may be used to indicate that anend user may not have access to a particular service or product or thatno more products of a particular type are available.

As another example of an indicator, it should be appreciated thataudible indicators may be used. For example, when an end user desires toselect an item to determine how many users have access to it, aparticular alert sound may be played to indicate that there are no moreitems of that kind or that the items available are below a threshold,etc.

An embodiment may provide how many times items were denied. An exemplaryscreenshot is provided in FIG. 6. For example, from a store point ofview, in-store analytics may determine and present what items areconstantly getting credit card denials or things of that nature. In FIG.6, a store manager can see that the first three items were denied only asmall number of times, especially because their color icon is green. Thestore manager can quickly determine that the last item was denied anexcessive number of times, because its icon color is red. Indeed, thefirst three items were denied 0, 2, and 2 times, respectively. The lastitem was denied five times.

An embodiment allows integrating in-store analytics with workflow. Forexample, an embodiment pertains to workflow by giving people access in acorporate environment. An embodiment provides one or more tools in abroad range of contexts, such as for example, being are part of an ITstore with mobile devices or also in an SAP environment provisioning SAPaccess or Oracle access or active directory, etc. In an embodiment, thecomputed analytical data shows or otherwise indicates how an object wasprocessed. This data enables users to understand how long an item takesto get, who has the item, how many times the item was requested, and soon. All and any of this data is useful to different end users based ontheir respective role in an organization.

An embodiment may be understood with reference to FIG. 7, which shows anexemplary screen shot of average number of days to complete a processregarding the item. For example, an embodiment may determine and presentthat some items may take longer than others for management to approve.In the embodiment, the configuration is based on weights and scales ofthe items. Each item has a sliding scale to determine where it is on thespectrum. Again referencing the high risk item, one can set the item tohave a 90 rating which based on a scale of 1-100, would be considered ahigh risk item. As another example, one item may be less valuable thananother item. Thus, the less valuable item may be given a weight of 25percent while the more valuable item may be given a weight of 75percent. As another example, the color of the icon of the first item canturn red when the average number of days to complete passes thethreshold of 10 days, whereas the color of the icon of the second itemcan turn red when the average number of days to complete passes thethreshold of 25 days. The Apple iPhone 5 shows not applicable (N/A) andthe color of the icon is grey, which is a result due to a particularrule setting.

Thus, when an embodiment is integrated with a workflow, for example ofan organization, an end user, such as a manager or executive, may sitdown and look at those workflows, e.g. the approval processes, and startto optimize his or her approval processes based on the computed anddisplayed analytical results. Alternatively, the computed analyticalresults can be used in an automated workflow to automate approvalprocesses regarding particular data, purchasing processes regardingparticular data, and other decision making processes regardingparticular data, thereby providing optimization and prioritization inthese workflow processes. As an example, user A who owns a section ofthe store can visit the store and determine if the item needs to beavailable based on requests, assignment, last request, etc. An end useror manager can determine if he or a direct report needs the item basedon others that have the item.

In an embodiment, the optimization may be performed automatically, forexample, by ingesting the data provided by the in-store analytics,performing particular analytics, and performing various optimizationalgorithms or rules based on such analytics. In an embodiment, a riskconfiguration is collapsed and weighted and then assigned to an item inthe store. The risk then is associated to a user. That is, similar tothe example above, the risk associated gives the user an idea of whatthe risk is. From an overall user risk score, that data is collapsedwhich provides an overall risk score for the item and the user.

An embodiment can be understood with reference to FIG. 8, which is asample screen shot showing the various risk scores assigned to eachitem. The last item has a risk score of 80 and shows two visualindicators that the risk score is high: the icon color is red and theicon has a picture of an exclamation point, indicating the important ofthe risk score.

For example, it can be reported to a direct report that an employee hasrequested a particular item an inordinate number of times, e.g. greaterthan a threshold value, or requested an item that the employee is notapproved to use. In this example, a high risk is associated to the user.

An embodiment may apply analytics and through such analytics allow anend user to determine that, with purchases or other requests, some itemstake longer to process, get through a particular process, etc.

In an embodiment, in-store analytics can be linked to, wholly or inpart, social media applications. In an embodiment, this data can beexposed to whoever needs it, e.g. via a web service reporting componentof a custom click report module. This exposing data process allows othersystems to use the exposed data and perform actions if required. Forexample, a particular item may be configured to be linked to a socialmedia application that provides consumer reviews of that particularproduct. For example, an end user may select a particular mobile deviceand further select a link to an online ratings application to determinewhether that particular item is popular and/or whether any problems withthe mobile device exist or are otherwise documented. For example, in anembodiment, particular products may be linked to or with Twitter,Facebook, or Yelp.

Persons of ordinary skill in the art will understand that apparatusesand methods in accordance with this invention may be practiced withoutthe specific details.

An Example Machine Overview

FIG. 9 is a block schematic diagram of a system in the exemplary form ofa computer system 900 within which a set of instructions for causing thesystem to perform any one of the foregoing methodologies may beexecuted. In alternative embodiments, the system may comprise a networkrouter, a network switch, a network bridge, personal digital assistant(PDA), a cellular telephone, a Web appliance or any system capable ofexecuting a sequence of instructions that specify actions to be taken bythat system.

The computer system 900 includes a processor 902, a main memory 904 anda static memory 906, which communicate with each other via a bus 908.The computer system 900 may further include a display unit 910, forexample, a liquid crystal display (LCD) or a cathode ray tube (CRT). Thecomputer system 900 also includes an alphanumeric input device 912, forexample, a keyboard; a cursor control device 914, for example, a mouse;a disk drive unit 916, a signal generation device 918, for example, aspeaker, and a network interface device 928.

The disk drive unit 916 includes a machine-readable medium 924 on whichis stored a set of executable instructions, i.e. software, 926 embodyingany one, or all, of the methodologies described herein below. Thesoftware 926 is also shown to reside, completely or at least partially,within the main memory 904 and/or within the processor 902. The software926 may further be transmitted or received over a network 930 by meansof a network interface device 928.

In contrast to the system 900 discussed above, a different embodimentuses logic circuitry instead of computer-executed instructions toimplement processing entities. Depending upon the particularrequirements of the application in the areas of speed, expense, toolingcosts, and the like, this logic may be implemented by constructing anapplication-specific integrated circuit (ASIC) having thousands of tinyintegrated transistors. Such an ASIC may be implemented with CMOS(complementary metal oxide semiconductor), TTL (transistor-transistorlogic), VLSI (very large systems integration), or another suitableconstruction. Other alternatives include a digital signal processingchip (DSP), discrete circuitry (such as resistors, capacitors, diodes,inductors, and transistors), field programmable gate array (FPGA),programmable logic array (PLA), programmable logic device (PLD), and thelike.

It is to be understood that embodiments may be used as or to supportsoftware programs or software modules executed upon some form ofprocessing core (such as the CPU of a computer) or otherwise implementedor realized upon or within a system or computer readable medium. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine, e.g. acomputer. For example, a machine readable medium includes read-onlymemory (ROM); random access memory (RAM); magnetic disk storage media;optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals, for example, carrierwaves, infrared signals, digital signals, etc.; or any other type ofmedia suitable for storing or transmitting information.

Further, it is to be understood that embodiments may include performingoperations and using storage with cloud computing. For the purposes ofdiscussion herein, cloud computing may mean executing algorithms on anynetwork that is accessible by internet-enabled or network-enableddevices, servers, or clients and that do not require complex hardwareconfigurations, e.g. requiring cables and complex softwareconfigurations, e.g. requiring a consultant to install. For example,embodiments may provide one or more cloud computing solutions thatenable users, e.g. users on the go, to view in-store analytics orconfigure in-store analytics on such internet-enabled or othernetwork-enabled devices, servers, or clients. It further should beappreciated that one or more cloud computing embodiments includein-store analytics or configuring in-store analytics using mobiledevices, tablets, and the like, as such devices are becoming standardconsumer devices.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

1. A computer-implemented method for generating and providing analyticsregarding target items to improve a user's experience in shopping,managing, or monitoring the target item, comprising the steps of:collecting and storing data about a target item; providing selectablemodes for sorting the target item data using an end user GUI capability;using said data and said selected mode, determining, by an analyticsengine, statistics about the target item; based on a configuration thatgives weighting and ranges to the data, displaying the target item andassociated statistics on a display responsive to a request for viewingthe target item, wherein the displayed target item is displayed with oneor more indicators, wherein an indicator indicates a particularimportance about a particular statistic; providing drill-down capabilityvia the display for presenting further detailed information about thetarget item; and responsive to a drill-down request, presenting thefurther detailed information about the target item; wherein the targetitem is a virtual or physical item that is be requested and assigned toone or more target users; whereby the displayed target item data areused in an organization to allow a suitable action to be performed; andwherein one or more steps are performed on at least a processor coupledto at least a memory.
 2. The method of claim 1, wherein the steps areconfigured to be performed automatically within a business organizationand wherein the suitable action is an automated process based on thestatistics regarding the target item.
 3. The method of claim 1, whereinthe end user is a human or machine that acts on the target item data orinteracts with the target item data.
 4. The method of claim 1, whereinthe target item is any of: stored assets; items available at the store;and representations of individual SAP accesses, Oracle accesses, oractive directory accesses.
 5. The method of claim 1, wherein themonitoring is used in auditing and compliance.
 6. The method of claim 1,wherein determining the statistics comprise: counting the number oftimes the item has been issued; counting the number of times the itemhas been requested; counting the number of times a request for the itemhas been denied; computing the item's age, e.g. how long the item hasbeen in the store; computing the average time to complete a request forsaid item; computing the days since last requested; determining who wasthe last requester; and computing a risk score for said item.
 7. Themethod of claim 1, further comprising incorporating the steps into athird party application by: adding GUI elements to a user interface ofthe third party application via HTML, CSS, and JavaScript changes; andproviding a back end web service to supply data to and from the thirdparty application to the analytics engines and storage; and using SQL tocompute the metrics from the supplied data.
 8. The method of claim 1,further comprising incorporating the steps into a third partyapplication by providing a particularly designed browser extension thataugments the online store of the third party target site to collect,store, and display the target item data and statistics.
 9. The method ofclaim 1, wherein the selectable modes are displayed as store analyticbuttons on the display.
 10. The method of claim 1, wherein customers areable to provide a configuration to suit their business needs.
 11. Themethod of claim 1, further comprising: providing a configurableinterface and application that allows a customer to configure the data,analytics, and display to suit their business needs by using a pointclick GUI web based configuration application.
 12. The method of claim1, wherein the statistics available for display to the end user and theconfiguration settings comprise: Item Age; Last Request; CurrentAssignment Count; Request Count; Denial Count; Average Request Days; andRisk Score.
 13. The method of claim 1, wherein the indicators availableare visual indicators for display to the end user and the configurationsettings comprise: Metric; Type (Static, Percent); Color Bands (Red,Yellow, Green); Range End; Default Sort (Descending, Ascending); andReverse.
 14. The method of claim 1, wherein the indicators comprise: anoverlaying special icon; and an audible indicator.
 15. The method ofclaim 1, wherein a risk configuration is collapsed and weighted and thenassigned to the target item and wherein the risk then is associated to auser.
 16. The method of claim 1, wherein the target item data andstatistics can be linked to social media applications via a web servicereporting component of a custom click report module that exposes thedata and allows other systems to use the exposed data to perform actionsas required.
 17. An apparatus for generating and providing analyticsregarding target items to improve a user's experience in shopping,managing, or monitoring the target item, comprising: at least oneprocessor operable to execute computer program instructions; at leastone memory operable to store computer program instructions executable bythe processor, for performing the steps of: collecting and storing dataabout a target item; providing selectable modes for sorting the targetitem data using an end user GUI capability; using said data and saidselected mode, determining, by an analytics engine, statistics about thetarget item; based on a configuration that gives weighting and ranges tothe data, displaying the target item and associated statistics on adisplay responsive to a request for viewing the target item, wherein thedisplayed target item is displayed with one or more indicators, whereinan indicator indicates a particular importance about a particularstatistic; providing drill-down capability via the display forpresenting further detailed information about the target item; andresponsive to a drill-down request, presenting the further detailedinformation about the target item; wherein the target item is a virtualor physical item that is be requested and assigned to one or more targetusers; and whereby the displayed target item data are used in anorganization to allow a suitable action to be performed.
 18. Anon-transitory computer readable medium having stored thereon a computerprogram for generating and providing analytics regarding target items toimprove a user's experience in shopping, managing, or monitoring thetarget item, said computer program comprising a program code which, whenexecuted by a processor, performs the steps of: collecting and storingdata about a target item; providing selectable modes for sorting thetarget item data using an end user GUI capability; using said data andsaid selected mode, determining, by an analytics engine, statisticsabout the target item; based on a configuration that gives weighting andranges to the data, displaying the target item and associated statisticson a display responsive to a request for viewing the target item,wherein the displayed target item is displayed with one or moreindicators, wherein an indicator indicates a particular importance abouta particular statistic; providing drill-down capability via the displayfor presenting further detailed information about the target item; andresponsive to a drill-down request, presenting the further detailedinformation about the target item; wherein the target item is a virtualor physical item that is be requested and assigned to one or more targetusers; and whereby the displayed target item data are used in anorganization to allow a suitable action to be performed.